Data Engineering

Your CPU is not slow. Your data is in the wrong place. Every cache miss your hot loop pays is not a hardware limitation, it is a layout decision that was never made consciously. The pointer your code chases, the struct field your loop skips over, the heap allocation that landed three megabytes from the last one: these are the real performance bottlenecks in CPU-bound systems, and none of them show up in a complexity analysis. This book teaches you to see your data the way the hardware sees it, measure what it costs, and redesign it so the memory system works with your code instead of against it.

PyTorch Deep Dive is a practical guide to mastering modern deep learning with PyTorch. From core concepts to advanced topics like transformers, diffusion models, and production deployment, it combines clear explanations, hands-on examples, and real-world best practices to help you build and scale AI applications with confidence.

The Databricks platform and data-engineering playbook for the engineers who own pipelines, govern catalogs, and keep workloads on schedule. Sixteen chapters on Unity Catalog, Lakeflow, identity, observability, and performance. Azure examples; concepts mapped to AWS and GCP.

A visuals-first guide to cracking data engineering interviews — SQL, data modeling, PySpark, Spark, Kafka, Databricks and Snowflake — and doing the work in production.

Data platform interviews test CDC, streaming, warehouse modeling, and trade-offs two levels deep — no generic system-design book covers it. This one does: 50 full mock interviews, 122 rapid-fire Q&A, and the framework that turns a vague prompt into a hire.

Explore data before you trust it. Data Exploration and Visualisation with R shows how to uncover structure, diagnose data-quality problems, challenge misleading patterns, and turn imperfect observations into credible evidence through thoughtful, reproducible visual analysis.

Stop only using databases — start understanding them. A living study of PostgreSQL internals for backend engineers: how pages, indexes, and WAL actually work, written clearly while learning, not after mastery.

Estimate honestly. Model fearlessly. This book turns T-shirt sizing into fast, team-friendly estimation and hands you a complete e-commerce warehouse build on BigQuery—star schemas, ETL pipelines, performance tuning and all. From decision frameworks to real-world trade-offs, you'll gain the confidence to plan realistically and architect data systems that scale.

Polars 2.0 changed defaults that don't raise errors: lazy queries now stream, and joins no longer keep row order. This short guide shows exactly what breaks when you upgrade from 1.x, with every example run under both 1.44.2 and 2.0.0 and the real output printed side by side.

Learn pandas, the most widely used data analysis library.

What I love about Dask is that it won't make us start from scratch. It doesn't ask us to learn a new language, adopt a new way of thinking, or rewrite five years of careful analysis into something completely different. It just takes the tools we've already got and quietly teaches them to work in parallel. The dataframe remains a dataframe. The array is still an array. The difference is that the work now spreads across eight cores, or eight hundred, and we barely had to change how we think.

AI can analyze the data. But who decides what the data means—and what to do next?Modern organizations have more information than ever, yet making good decisions remains difficult.AI-Powered Smart Decision Making shows how to combine AI, data, analytics, and human judgment to turn information into insight and insight into action.Discover a practical framework for making better decisions across strategy, marketing, sales, operations, finance, risk, and beyond.AI is not the decision. AI improves the decision process. Table of ContentsIntroduction—The New Era of AI-Powered Decision MakingChapter 1 — The Foundations of Better Business DecisionsChapter 2 — From Intuition to Data-Driven Decision MakingChapter 3 — Understanding Data for Better DecisionsChapter 4 — Business Analytics: Turning Data into InsightChapter 5 — How AI Changes Business Decision MakingChapter 6 — Building an AI-Powered Decision FrameworkChapter 7 — Asking the Right Questions: AI, Data & Decision ProblemsChapter 8 — Predictive Decision MakingChapter 9 — Prescriptive Analytics and Decision OptimizationChapter 10 — AI for Strategic Business DecisionsChapter 11 — AI for Marketing and Customer DecisionsChapter 12 — AI for Sales, Operations and Supply ChainChapter 13 — AI for Financial and Risk DecisionsChapter 14 — Human + AI: The New Decision-Making PartnershipChapter 15 — Bias, Uncertainty and the Limits of AI DecisionsChapter 16 — Trustworthy, Responsible and Explainable AI DecisionsChapter 17 — Measuring Decision Quality and Business ImpactChapter 18 — Building an AI-Powered Decision CultureChapter 19 — Designing an AI-Powered Decision SystemChapter 20 — The Future of Smart Decision MakingConclusion — From Data to Better DecisionsDisclaimer

The ultimate technical blueprint for developers and AI engineers to strip away execution overhead, leverage hardware-accelerated NumPy/PyTorch operations, and scale Python code effortlessly

Running ClickHouse at petabyte scale takes more than knowing SQL. This practical guide shows experienced engineers how to design, deploy and operate production clusters, from data modeling and ingestion to Kubernetes, query tuning, observability, disaster recovery and cost control, with real-world patterns and concrete configurations throughout.

Your data warehouse holds the answers — LLMs can help you ask better questions. This practical guide shows data professionals how to build text-to-SQL pipelines, enrich warehouse data with AI, and bring semantic search to their existing tables.